Several studies evaluated the consistency of covariance and FPCA estimators
for incomplete curve settings.
When incompleteness originates from a missing completely at random (MCAR) process
and measurements are dense,
established estimators for the mean, the covariance and for eigenfunctions and eigenvalues
are consistent [1]}.
For the subject-specific functional principal component (FPC) scores,
[1]} introduced a consistent estimator.
His comparison to the PACE approach of [3]}
indicates that the bias of comparable conditional methods for
estimating the FPC scores is likely to be small.
